Long only 1/n portfolio

import pandas as pd
pd.options.plotting.backend = "plotly"

import yfinance as yf

from cvx.simulator.builder import builder
from cvx.simulator.grid import resample_index

data = yf.download(tickers = "SPY AAPL GOOG MSFT",  # list of tickers
                   period = "10y",                   # time period
                   interval = "1d",                 # trading interval
                   prepost = False,                 # download pre/post market hours data?
                   repair = True)                   # repair obvious price errors e.g. 100x?

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[**********************50%                       ]  2 of 4 completed

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prices = data["Adj Close"]

capital = 1e6

b = builder(prices=prices, initial_cash=capital)

for time, state in b:
    # each day we invest a quarter of the capital in the assets
    b[time[-1]] = 0.25 * state.nav / state.prices

portfolio = b.build()
portfolio.profit.cumsum().plot()

portfolio.nav.plot()

Rebalancing

Usually we would not execute on a daily basis but rather rebalance every week, month or quarter. There are two approaches to deal with this problem in cvxsimulator.

• Resample the existing daily portfolio (helpful to see effect of your hesitated trading)

• Trade only on days that are within a predefined grid (most flexible if you have a rather irregular grid)

Resample an existing portfolio

portfolio_resampled = portfolio.resample(rule="M")

frame = pd.DataFrame({"original": portfolio.nav, "monthly": portfolio_resampled.nav})
frame

	original	monthly
Date
2013-07-17	1.000000e+06	1.000000e+06
2013-07-18	9.979712e+05	9.979712e+05
2013-07-19	9.621811e+05	9.623698e+05
2013-07-22	9.718759e+05	9.717556e+05
2013-07-23	9.639049e+05	9.637599e+05
...	...	...
2023-07-11	7.743600e+06	7.728321e+06
2023-07-12	7.835525e+06	7.820110e+06
2023-07-13	7.976058e+06	7.960521e+06
2023-07-14	8.005296e+06	7.990038e+06
2023-07-17	8.011359e+06	7.995173e+06

2517 rows × 2 columns

print(portfolio_resampled.stocks)

                    AAPL          GOOG         MSFT          SPY
Date                                                            
2013-07-17  18736.925754  10927.529417  8394.131742  1788.326108
2013-07-18  18736.925754  10927.529417  8394.131742  1788.326108
2013-07-19  18736.925754  10927.529417  8394.131742  1788.326108
2013-07-22  18736.925754  10927.529417  8394.131742  1788.326108
2013-07-23  18736.925754  10927.529417  8394.131742  1788.326108
...                  ...           ...          ...          ...
2023-07-11  10225.578048  16323.945419  5822.701490  4434.563154
2023-07-12  10225.578048  16323.945419  5822.701490  4434.563154
2023-07-13  10225.578048  16323.945419  5822.701490  4434.563154
2023-07-14  10225.578048  16323.945419  5822.701490  4434.563154
2023-07-17  10225.578048  16323.945419  5822.701490  4434.563154

[2517 rows x 4 columns]

# almost hard to see that difference between the original and resampled portfolio
frame.plot()

# number of shares traded
portfolio_resampled.trades_stocks.iloc[1:].plot()

Trade only days in predefined grid

b = builder(prices=prices, initial_cash=capital)

# define a grid
grid = resample_index(prices.index, rule="M")

for time, state in b:
    # each day we invest a quarter of the capital in the assets
    if time[-1] in grid:
        b[time[-1]] = 0.25 * state.nav / state.prices
    else:
        # forward fill an existing position
        b[time[-1]] = b[time[-2]]
        
portfolio = b.build()

portfolio.nav.plot()

# Trading only once a month can lead to days where 150k had to be reallocated
portfolio.turnover.iloc[1:].plot()

Why not resampling the prices?

I don’t believe in bringing the prices to a monthly grid. This would render it hard to construct signals given the sparse grid. We stay on a daily grid and trade once a month.